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Social group optimization for global optimization of multimodal functions and data clustering problems

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Abstract

Cost and physical constraints in the engineering applied problems obligate finding the best results that global optimization algorithms cannot realize. For accurate and faster optimization, switching between known multiple local/global solutions is necessary. The current work proposed a social group optimization (SGO) for solving multimodal functions as well as data clustering problems. For solving global optimization problems, the SGO inspired by the social behavior of human toward solving a complex problem was applied. The SGO is a population-based optimization algorithm using solution population to reach global solution. The simulation results compared its performance with eight particle swarm optimizer variants. The results demonstrated good performance of the SGO.

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Authors and Affiliations

  1. MITS, Hyderabad, India

    Anima Naik

  2. ANITS, Vishakapatnam, India

    Suresh Chandra Satapathy

  3. Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Gharbia Governorate, Egypt

    Amira S. Ashour

  4. Department of Information Technology, Techno India College of Technology, Kolkata, India

    Nilanjan Dey

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  1. Anima Naik
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  2. Suresh Chandra Satapathy
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  3. Amira S. Ashour
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  4. Nilanjan Dey
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Correspondence to Amira S. Ashour.

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Naik, A., Satapathy, S.C., Ashour, A.S. et al. Social group optimization for global optimization of multimodal functions and data clustering problems. Neural Comput & Applic 30, 271–287 (2018). https://doi.org/10.1007/s00521-016-2686-9

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